{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 159/159 [07:27<00:00,  2.81s/it]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os, json\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "# generate tracklets\n",
    "annot_dir = 'annotation/training'\n",
    "tracklets = {}\n",
    "for folder in tqdm(os.listdir(annot_dir)):\n",
    "    for video_json in os.listdir(os.path.join(annot_dir, folder)):\n",
    "        video_idx = folder + '/' + video_json.split('.')[0]\n",
    "#         if video_idx != '0002/2739554651':\n",
    "#             continue\n",
    "        with open(os.path.join(annot_dir, folder, video_json), 'r') as f:\n",
    "            annot = json.load(f)\n",
    "#         import pdb; pdb.set_trace()\n",
    "        \n",
    "        tracklets_temp = {}\n",
    "        for instance in annot['subject/objects']:\n",
    "            if instance['category'] in ['adult', 'child', 'baby']:\n",
    "                tracklets_temp[instance['tid']] = {}\n",
    "                tracklets_temp[instance['tid']]['bbox'] = []\n",
    "                tracklets_temp[instance['tid']]['frames'] = []\n",
    "        for frame_id, trajectory in enumerate(annot['trajectories']):\n",
    "            for instance in trajectory:\n",
    "                if instance['tid'] in tracklets_temp:\n",
    "                    coordinates = [\n",
    "                        (instance['bbox']['xmin'] + instance['bbox']['xmax']) / 2, # center_x\n",
    "                        (instance['bbox']['ymin'] + instance['bbox']['ymax']) / 2, # center_y\n",
    "                        instance['bbox']['xmax'] - instance['bbox']['xmin'], # width\n",
    "                        instance['bbox']['ymax'] - instance['bbox']['ymin'], # height\n",
    "                    ]\n",
    "                    tracklets_temp[instance['tid']]['bbox'].append(coordinates)\n",
    "                    tracklets_temp[instance['tid']]['frames'].append(frame_id)\n",
    "        \n",
    "        # transform into ndarray\n",
    "        for instance in tracklets_temp:\n",
    "            tracklets_temp[instance]['bbox'] = np.array(tracklets_temp[instance]['bbox'], dtype=np.float64)\n",
    "            tracklets_temp[instance]['frames'] = np.array(tracklets_temp[instance]['frames'])\n",
    "        \n",
    "        tracklets[video_idx] = tracklets_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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'1007/9307201044', '1007/4553964463', '1007/8608466102', '1007/4311757255', '1007/13941074421', '1007/3448707252', '1007/6971584563', '1007/5596908548', '1007/4036544038', '1007/3392902718', '1007/2740354629', '1007/5309090557', '1007/8641577333', '1007/3327561568', '1007/4959773843', '0023/6971535794', '0023/2402163364', '0023/5635807497', '0023/6888325779', '0023/3260805075', '0023/4890982174', '0023/2549562657', '0023/6542819149', '0070/7883814230', '0070/5839540211', '0070/3793953104', '0030/10111281613', '0030/2857624971', '0030/2622708989', '0030/2677153721', '0030/2516045520', '0030/4904173487', '0030/8727507710'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracklets.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[111,  13,  48, 106],\n",
       "       [111,  13,  48, 106],\n",
       "       [112,  12,  47, 106],\n",
       "       ...,\n",
       "       [111,  28,  48,  91],\n",
       "       [110,  26,  49,  93],\n",
       "       [111,  25,  48,  94]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracklets['0000/3418738633'][0]['bbox']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('train_tracklets.json', 'w') as f:\n",
    "#     json.dump(tracklets, f)\n",
    "\n",
    "import pickle\n",
    "with open('train_tracklets.pkl', 'wb') as f:\n",
    "    pickle.dump(tracklets, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 131/131 [00:53<00:00,  2.45it/s]\n"
     ]
    }
   ],
   "source": [
    "# generate tracklets\n",
    "annot_dir = 'annotation/validation'\n",
    "tracklets = {}\n",
    "for folder in tqdm(os.listdir(annot_dir)):\n",
    "    for video_json in os.listdir(os.path.join(annot_dir, folder)):\n",
    "        video_idx = folder + '/' + video_json.split('.')[0]\n",
    "#         if video_idx != '0002/2739554651':\n",
    "#             continue\n",
    "        with open(os.path.join(annot_dir, folder, video_json), 'r') as f:\n",
    "            annot = json.load(f)\n",
    "#         import pdb; pdb.set_trace()\n",
    "        \n",
    "        tracklets_temp = {}\n",
    "        for instance in annot['subject/objects']:\n",
    "            if instance['category'] in ['adult', 'child', 'baby']:\n",
    "                tracklets_temp[instance['tid']] = {}\n",
    "                tracklets_temp[instance['tid']]['bbox'] = []\n",
    "                tracklets_temp[instance['tid']]['frames'] = []\n",
    "        for frame_id, trajectory in enumerate(annot['trajectories']):\n",
    "            for instance in trajectory:\n",
    "                if instance['tid'] in tracklets_temp:\n",
    "                    coordinates = [\n",
    "                        (instance['bbox']['xmin'] + instance['bbox']['xmax']) / 2, # center_x\n",
    "                        (instance['bbox']['ymin'] + instance['bbox']['ymax']) / 2, # center_y\n",
    "                        instance['bbox']['xmax'] - instance['bbox']['xmin'], # width\n",
    "                        instance['bbox']['ymax'] - instance['bbox']['ymin'], # height\n",
    "                    ]\n",
    "                    tracklets_temp[instance['tid']]['bbox'].append(coordinates)\n",
    "                    tracklets_temp[instance['tid']]['frames'].append(frame_id)\n",
    "        \n",
    "        # transform into ndarray\n",
    "        for instance in tracklets_temp:\n",
    "            tracklets_temp[instance]['bbox'] = np.array(tracklets_temp[instance]['bbox'], dtype=np.float64)\n",
    "            tracklets_temp[instance]['frames'] = np.array(tracklets_temp[instance]['frames'])\n",
    "        \n",
    "        tracklets[video_idx] = tracklets_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('val_tracklets.pkl', 'wb') as f:\n",
    "    pickle.dump(tracklets, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "slowfast",
   "language": "python",
   "name": "slowfast"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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